How commodity businesses can benefit from artificial intelligence and machine learning

To navigate today’s evolving and volatile commodity markets, businesses need commodity trading and risk management (CTRM) solutions to manage their marketing, trading, scheduling, and accounting activities and data. With all this information in a single solution, they can evaluate their risks, portfolio values, imbalances, costs, and profitability at the enterprise level and drill down to individual deals and even tanks for more informed decision making.

CTRMs also automate resource-consuming processes, like recording logistics activities, generating invoices, and calculating mark-to-market, exposure, and value-at-risk. Businesses with CTRMs realize significant efficiencies and process control improvements, and, most importantly, gain data-driven insights.

While businesses that utilize CTRMs already have significant advantages over commodity companies that rely only on Excel and ERP systems, they can even further distance themselves from their competitors by leveraging tools that utilize the data already available in their CTRMs.

The future of artificial intelligence and machine learning in commodity management

In the next phase of their digital transformations, commodity organizations are looking to streamline their business processes to further recognize operational efficiencies and to optimize their decisions to capitalize on opportunities. By adopting innovative artificial intelligence (AI) and machine learning (ML) technologies integrated with their CTRMs, commodity businesses can do just that.

AI and ML are not new technologies to financial firms. In fact, many businesses that commodity companies already have relationships with (such as counterparties, lenders, and brokers) have already deployed AI and ML to automate critical business functions, like fraud detection. With the efficiencies these technologies have already brought, companies will be looking to extend AI and ML to even more complex activities in the future.

Five practical uses for artificial intelligence and machine learning in commodity management

As AI and ML continue to become more commonplace in other financial markets, the commodity and energy industries will adopt these technologies as well. Below are just a few processes that AI and ML could greatly improve for commodity firms:

1. Forecasting complex prices to determine the most profitable time and place to purchase and sell commodities.
2. Validating logistics data and identifying receipt and delivery trends as they are occurring, as well as the exact data elements requiring additional research.
3. Forecasting expected receipts, deliveries, and imbalances early so one has time and optionality to respond.
4. Determining the optimal path to market and receipt and delivery dates across complex supply chains.
5. Improving regulatory compliance by identifying patterns that require reporting or could be a potential issue, like implied transactions.

With new complexities arising from energy transition and the increasing frequency of major market disruptions, commodity firms can benefit now more than ever from the analytical insights and business optimization that AI and ML provide.

Incorporating artificial intelligence and machine learning into your commodity business

Artificial intelligence and machine learning will revolutionize the commodities industry. It’s no longer a matter of if commodities businesses will adopt these innovative technologies, but when and how. In Oilman’s “5 Ways Artificial Intelligence and Machine Learning Benefit Crude Oil Marketers,” ION’s Josh Gray recommends laying the groundwork for AI and ML today.

“Commodity organizations are just now beginning to explore the possibilities that artificial intelligence and machine learning offer their businesses,” says Gray. “Alone, this can be a daunting task, but the value of AI and ML compounds over time, driving tremendous growth for organizations with each instance. That’s why industry visionaries are turning to the leaders in advanced commodity analytics to lay a technological foundation that embraces ML today to support more significant AI initiatives in the future. Those with the head start will have the competitive advantage over the mainstream laggards.”

While incorporating AI and ML can be daunting to some commodity businesses, commodity management solutions at the forefront of advanced analytics, like ION’s FEA, can ease the transition. ION’s portfolio of advanced analytical solutions empowers forward-looking commodity firms to learn from their data, identify patterns, and make intelligent decisions with minimal intervention.

At ION, we are constantly innovating our portfolio of commodity management solutions with the latest technologies and are looking forward to the benefits AI and ML can bring to our community.